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INDONESIA
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Published by Politeknik Statistika STIS
ISSN : 28099842     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Conference on Data Science and Official Statistics International Conference on Data Science and Official Statistics (ICDSOS) 2023 is organized by Politeknik Statistika STIS and Statistics Indonesia (BPS). This international conference in collaboration with Forum Pendidikan Tinggi Statistika (FORSTAT), Ikatan Statistisi Indonesia (ISI), United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), and United Nations Statistics Division (UNSD). The ICDSOS will bring together statisticians and data scientists from academia, official statistics, health sector and business, junior and senior professionals, in an inviting hybrid environment on November 24th - 25th, 2023. Dealing with the theme of this conference is Harnessing Innovation in Data Science and Official Statistics to Address Global Challenges towards the Sustainable Development Goals. DATA SCIENCE Machine Learning and Deep Learning Data Science and Artificial Intelligence (AI) Data Mining and Big Data Statistical Software Information System Development for Official Statistics Remote Sensing to Strengthen Official Statistics Other data science relevant topic APPLIED STATISTICS Applied Multivariate Analysis Applied Time Series Analysis Applied Spatial Statistics Applied Bayesian Statistics Microeconomics Modelling and Applications Macroeconomics Modelling and Applications Econometrics Modelling and Applications Quantitative Public Policy and Statistical Analysis Applied Statistics on Demography Applied Statistics on Population Studies Applied Statistics on Biostatistics and Public health Other applied statistics relevant topic OFFICIAL STATISTICS Official Statistics Survey Methodology Developments Data Collection Improvements Sustainable Development Goals (SDGs) Indicators Estimation Small Area Estimation (SAE) Non Response and Imputation Methods Sampling Error and Non Sampling Error Evaluation Benchmarking Regional Official Statistics Other official statistics relevant topic
Arjuna Subject : Umum - Umum
Articles 151 Documents
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Micro and Macro Determinants of Precarious Employment in Indonesia: An Empirical Study of Paid Workers using Multilevel Binary Logistic Regression Mohammad Rifky Pontoh; Nucke Widowati Kusumo Projo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.68

Abstract

Decent work for all is one of the goals stated in the Sustainable Development Goals (SDGs). One indicator that can represent proper work conditions is the precarious employment rate (PER). In recent periods, the precarious employment rate in Indonesia has shown an increasing trend. It indicates a decent work deficit in Indonesia. In addition, the PER among provinces has a different figure. This study aims to analyze the micro and macro factors that influence the status of precarious employees in Indonesia. The analytical method used in this study is multilevel binary logistic regression. The results show that micro factors; namely the worker's characteristics, including age, education level, employment sector, previous work status, and urban-rural area; have a significant effect on the precarious status of employees. In terms of macro factors, it is found that an increase in the output of the industrial and construction sectors can reduce the tendency of a worker to become a precarious employee. Meanwhile, an increase in labour supply increases the likelihood of workers becoming precarious employees. Various parties, including society and government, have to put extra efforts to reduce the precarious employment rate by improving the quality of human capital and domestic products demand.
R Package Development for Difference Benchmarking in Small Area Estimation Fay-Herriot Model Zaza Yuda Perwira; Azka Ubaidillah
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.69

Abstract

In recent decades, the use of small area estimation (SAE) for producing official statistics has been widely recognized by many National Statistics Offices including BPS-Statistics Indonesia. For official statistics usage, the aggregation of small area estimates is expected to be numerically consistent and more efficient than the aggregation of the unbiased direct estimates that cannot be guaranteed by Fay-Herriot model. Simulation experiments are performed to assess the behaviour of the difference benchmarking method Fay-Herriot model and to compare the mean squared error (MSE). The result shows that the difference benchmarking method can produce a consistent aggregation towards the direct estimation. Furthermore, an R package was built to implement the method that is easier to be used and is already available in the CRAN website. The package has been evaluated using validity (simulation), performance, case studies, and usability tests. These evaluations show that the package is suitable for use. Implementation of the methodology is also be applied to estimate average household consumption per capita expenditure in districts in D.I. Yogyakarta province, Indonesia 2019
Application of The Sequential Hot-deck Imputation Method for Identification of Indonesian Standard Classification of Business Fields (KBLI) Iman Jihad Fadillah; Chaterina Dwi Puspita
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.70

Abstract

The Covid-19 pandemic requires the adjustment of new habits in daily life, including in a series of data collection processes. One of the new adjustments is to use alternative types of data collection other than face-to-face, such as the telephone and the web. Information collected through telephone interviews is less accurate than the same information collected through face-to-face interviews, such as the level of non-response, consistency between entries, and outliers in the data or often identified as missing values. Missing value will be very influential on data quality when it appears on important variables. One of these variables is the Standard Classification of Business Fields (KBLI). Imputation is one method that can be used to deal with this problem. One method that is quite popular is Sequential Hot-deck Imputation. Therefore, this study aims to facilitate the identification of 5-digit KBLI by utilizing the Sequential Hot-deck Imputation method. The results of this study indicate that the use of the Sequential Hot-deck Imputation method in the KBLI identification process gives very high accuracy results. In addition, the use of this method is very efficient in the identification process, because the time required is very short, even in large datasets.
Entity Matching of Shop Accounts in Online Commerce Portals Dina Salsabila; Takdir Takdir
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.71

Abstract

Currently, online marketplace data are valuable data sources to be analyzed forvarious purposes. In the data collecting phases, duplication of shop accounts was found, resulting in biased analysis. This study examines the development of a mechanism to identify duplicate entities, i.e. store accounts, between different online marketplaces, or commonly known as entity matching. Word similarity algorithms were adopted as the core elements of our approach. Additionally, we present an entity matching model by examining logisticregression, naive Bayes, and random forest to find the best model for classifying store account similarities. Top online marketplaces in Indonesia are the object of our study, limited to one developing municipality, i.e. Sleman, DI Yogyakarta. The results show the best model has an accuracy value of 0.961, precision of 0.963, a recall of 0.958, and an F1-score of 0.962. Therefore, these results are acceptable for duplicate identification.
Study of Search Algorithm Optimization from Multi-Version Data Warehouse using NoSQL Non-relational Database Lutfi Rahmatuti Maghfiroh; Ramadhan Azizulhakim Yusuf
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.74

Abstract

Statistics Indonesia, which produces large-scale data, requires effective and optimal storage. Research related to Multi-Version Data Warehouse (MVDW), which utilizes document-based NoSQL itself, has attempted to be developed for the sake of BPS data storage and proposed an algorithm to store and search data. This paper is made to examine algorithm optimization methods to reduce the time used in the process of storing and searching data when needed. The algorithm proposed in this paper focuses on the data storage process by suggesting a storage model that generalizes the coding of variables in the data warehouse used so that later data searches can be carried out more easily and optimally. Other optimization methods are also carried out by applying query optimization methods to support and improve the optimization of the proposed algorithm. The results of the two optimization methods carried out can be said to be successful because the time used in the data search process by utilizing the algorithm after the application of the optimization method has been reduced when compared to the data search process using algorithms that have been developed by previous research.
Development of Question Answering System for Public Relation Division in Student Admission Lutfi Rahmatuti Maghfiroh; Wahyudi Syahputra; Ibnu Santoso
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.81

Abstract

Politeknik Statistika STIS (Polstat STIS) holds the new students' admission (PMB) every year which aims to gather, test, and, select all of its applicants who want to continue their study at STIS. STIS establish a committee during this event named Public Relation (PR) Division. PR Division to be intermediaries between STIS and the applicants. One of many PR Division tasks is to reply to all the questions from applicants about administration, procedure, or other things about PMB and STIS. PR Division is facing some problems that can hinder its performance to do the tasks. How do we address the problem is the reason that this research begins in the first place. The goal of this research is to build and establish a web-based system that is capable to solve all the problems the current system has. The system is divided into two main functions, the first one is FAQ management by PR Division members. The other function is a chatbot that automatically answers the question by using the TF-IDF algorithm. The conclusion on all testing and evaluation is the system that being build is already fulfilled all its requirements also the system is feasible to be used.
The Effect of Shifting Large and Medium-Sized Industry Agglomeration on the Economic Development in Kanti Region in 2003-2018 Ahmad Firman Maulana; Ekaria Ekaria
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.83

Abstract

The development of real Gross Domestic Regional Product (GDRP) 2010 of all cities in Kanti region increased during 2003-2018. However, when viewed the growth rate in aggregate, it slowed during the period 2010-2018. One of the causes is the shift of large and medium-size industry (LMI) agglomeration from Kanti region to Kangga region. This study aims to find out the location and the dynamics of the shift of LMI agglomeration using the Hoover-Balassa index that is presented through thematic maps. In addition, the study also analyses the effect of the shift of LMI agglomeration and other factors on economic growth in Kanti region using the regression analysis of panel data. The individual units used are five administrative cities in the Kanti region with annual units from 2003 to 2018. Fixed effect model with seemingly unrelated regression (FEM-SUR) is used to estimate the parameters of the economic growth model in Kanti region. The results showed that Kanti region was agglomerated in North Jakarta and East Jakarta. Labor-intensive potential factor has a negative and significant effect, while the labor productivity of LMI and domestic investment has a positive and significant effect on economic growth in Kanti region. North Jakarta is an area that despite the shift of LMI agglomeration but still able to increase its economic growth, while East Jakarta has decreased. So, the Provincial Government of Jakarta need to adapt the implementation of LMI agglomeration in North Jakarta to encourage economic growth in East Jakarta and West Jakarta in accordance with regional spatial planning for industry.
Demographics Characteristics of Smoker in Poor Households in Riau Islands Province Dio Dwi Saputra
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.85

Abstract

Smoking habits in Indonesia have been formed since the colonial era. Smoking habits that need attention are in poor households. In 2020, Riau Islands Province as the one of youngest provinces in Indonesia has a smoking prevalence of 26.16% and the percentage of poor people is 5.92%. This condition is the basis for researchers to conduct a study that aims to determine the demographics characteristics of smokers. This study uses raw data from the National Socio-Economic Survey (SUSENAS) in Riau Islands Province in March 2020. The variables used are smoking status, gender, age group, education level, region, and recent migrant. The output of the processing stage is that the prevalence of smoking will be greater in the male population (OR = 132.04), the age group of 46-65 (OR = 4.77), the age group of 66 and over (OR = 2.11), the junior high school level (OR = 4.66), the senior high school level (OR = 5.98), the college level (OR = 3.13), living in the urban area (OR = 1.22) and the recent migrant (OR = 3.12). Thus, it is necessary to make a specific policy following the above characteristics in reducing smoking habits among poor households.
Covid-19 Vaccination: Health and Economic Correlations Zaky Musyarof; Indira Nur Qomari
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.87

Abstract

Vaccination program is an important strategy in eradicating Covid-19 pandemic. Vaccination can intervene to accelerate the formation of herd immunity. When herd immunity is formed later, it is believed that the Covid-19 virus will gradually eradicated. Furthermore, economic activity will return to normal. Then, has the vaccination program run by the Indonesian government had an impact on health and economic recovery? Some claim that this vaccination program has had a positive impact. However, in-depth research is felt to be done to really look at this impact. As a first step, it is necessary to look at the relationship between vaccination, health and economic development. This relationship will be an early indication of whether the vaccination program is successful or not. In fact, vaccination was strongly correlated with a decrease in the transmission of new cases and moderately correlated with the recovery rate. Overall, vaccination is strongly correlated with health based on the canonical correlation. Meanwhile, for the economy, vaccination has a weak correlation with the poverty rate and Gini ratio. However, overall based on the canonical correlation, vaccination is strongly correlated with the economy. Furthermore, the development of tourism shows an indication of a correlation with vaccination.  
The Effect of the Digital Economy on Indecent Work in Indonesia 2019 Yuniar Putri Awaliyah Risky; Nucke Widowati Kusumo Projo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.88

Abstract

The emergence of the digital economy is indicated to affect the employment sector. The job opportunities created by the digital economy provide an opportunity for workers to work in poor jobs, full of risks and indecent works. This study aims: first, to describe the economic digital and indecent work conditions in Indonesia. Second, to investigate the direct influence of infrastructure and digital media on the digital economy. Third, to examine the direct impact of the digital economy on indecent work. The data used is secondary data with observations from 34 provinces sourced from BPS and other ministries. Using the SEM-PLS analysis method, the results show that infrastructure and digital media positively impact the digital economy. Similarly, the digital economy, reflected by e-commerce sellers and buyers, has a positive and significant relationship to indecent work as reflected by Employment Excessive Working Time (EEWT), Precarious Employment Rate (PER), and non-union workers. It can be said that the increase in the digital economy influences the conditions of indecent work.

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